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Spotlight on Economics: Need for Risk Analysis in Research and Teaching

Risk analysis is a major and vital component for short- and long-term successful growth and profitability.

By Saleem Shaik, Assistant Professor

NDSU Agribusiness and Applied Economics Department

For any farm or agribusiness firm, risk analysis is a major and vital component for short- and long-term successful growth and profitability.

New-generation farmers and agribusiness firms are short of experience with facing risk and uncertainty, the same as farmers from earlier generations. So, in general, it is important to understand the meaning (definition and, most importantly, underlying theory), context (issues faced during the oil crisis, land bubble and financial crisis) and implications (delinquencies, financial investment losses and bankruptcies) when faced with risks or uncertain events.

Risk is the “possibility of loss.” Risk arises from some uncertain outcomes being less favorable than others. The different levels of risks are normal, intermediate and catastrophic.

To look at risks, we do a risk analysis. Risk analysis is a blend of art and science that has risk assessment, risk management and risk communication as its three pillars.

Risk assessment involves identifying risk, followed by an accurate measurement of risk. Risk identification is an interactive process to identify the different kinds of risks associated with the agriculture sector, agribusiness firm or food industry.

For example, there are several different kinds of risk faced by a producer on the farm. Production risk is the uncertainty about what crop/livestock production or input resources to use due to weather, pests, diseases and other causes. Price risk is the uncertainty about output and input prices and changes in the demand and supply of inputs and outputs.

Financial risk is borrowed money from banks. Interest rates, repayment capacity and credit availability could lead to changes in profitability, solvency and liquidity ratios. Institutional risk is the uncertainty in government policies such as farm programs, crop insurance and changes in tax laws and regulations. Human risk is health and relationships, accidents, illnesses, nonfarm income and generational transitions.

Risk measurement requires an estimation and computation of the distribution of the risks described above. Distribution involves the estimation and computation of levels (averages), variations or risk (standard deviations, coefficient of variation), downside risk (semistandard deviations) and extreme events (skewness and kurtosis). Also, there is a need to estimate the probability (frequency and magnitude) of alternative outcomes that may affect the well-being of the decision maker.

Other analyses include the computation of individual and cumulative density functions and general statistical regression analysis. Even though risk measurement is skewed toward statistical concepts, it can be easily estimated in an Excel spreadsheet or new apps. Even though it is frowned upon, this needs to be understood and implemented in both research and teaching. Once risks faced by farmers or agribusinesses are assessed, it is imperative to develop alternative risk management tools. This would be specific to a particular risk. Under certain conditions, risk management tools can be applied to a broader set of risks. Also, there is a need to focus on the process of weighing which policy alternative risk management tools to accept to minimize or reduce risks and select/implement appropriate strategic options.

Risk communication is the final pillar of risk analysis. It is important to convey the short- and long-term implications of farm or agribusiness risk analysis to the academic community, policymakers, clientele groups, food industry and consumers.

My research last year revolved around production and policy risk analyses related to the North Dakota, northern Great Plains, U.S. and international agricultural sectors. Specifically, my research dealt with production or yield risks and how these affect the farm and agricultural sector.

To accomplish this objective, for the last three years, my research involved collecting and constructing farm, county, state, regional and national outputs and then input and creating a total factor productivity database utilizing statistical econometric mathematical programming. Further, this included developing farm program, crop insurance and public investment (research and Extension Service expenditures) databases for the North Dakota, northern Great Plains states (North Dakota, South Dakota, Minnesota, Nebraska, Montana and Kansas) and U.S. agriculture sector for 1933 through 2009.

With respect to risk management, my research revolves around crop insurance demand and asymmetric issues including adverse selection and moral hazard and estimating actuarially sound premium rates using the National Agricultural Statistics Service and Risk Management Agency historical databases.

The outcome of my research has implications for farmers or producers. For example, the availability of actuarially sound premium rates will lead to reduced costs to low-risk farmers and taxpayers, benchmarking of efficiency scores and factors affecting the efficiency score benchmarks.

Another example is the farm sector and industry. Changes in the efficiency and productivity of the state’s agricultural sector can cause institutional and trade changes. Also, are farm programs and crop insurance causing farmers to use less or more specific inputs and increasing production?

Lastly, changes in the efficiency and productivity of the state’s agricultural sector can play a role in the extent and direction of public investments by policymakers.

NDSU Agriculture Communication - Aug. 15, 2012

Source:Saleem Shaik, (701) 231-7459, saleem.shaik@ndsu.edu
Editor:Rich Mattern, (701) 231-6136, richard.mattern@ndsu.edu
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